Earth’s Energy Imbalance

2014 ◽  
Vol 27 (9) ◽  
pp. 3129-3144 ◽  
Author(s):  
Kevin E. Trenberth ◽  
John T. Fasullo ◽  
Magdalena A. Balmaseda
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Radiative Forcing ◽  
Heat Content ◽  
Volcanic Eruptions ◽  
Climate Change Signal ◽  
Ocean Reanalysis ◽  
Climate System Model ◽  
Energy Imbalance ◽  
Rates Of Change

Abstract Climate change from increased greenhouse gases arises from a global energy imbalance at the top of the atmosphere (TOA). TOA measurements of radiation from space can track changes over time but lack absolute accuracy. An inventory of energy storage changes shows that over 90% of the imbalance is manifested as a rise in ocean heat content (OHC). Data from the Ocean Reanalysis System, version 4 (ORAS4), and other OHC-estimated rates of change are used to compare with model-based estimates of TOA energy imbalance [from the Community Climate System Model, version 4 (CCSM4)] and with TOA satellite measurements for the year 2000 onward. Most ocean-only OHC analyses extend to only 700-m depth, have large discrepancies among the rates of change of OHC, and do not resolve interannual variability adequately to capture ENSO and volcanic eruption effects, all aspects that are improved with assimilation of multivariate data. ORAS4 rates of change of OHC quantitatively agree with the radiative forcing estimates of impacts of the three major volcanic eruptions since 1960 (Mt. Agung, 1963; El Chichón, 1982; and Mt. Pinatubo, 1991). The natural variability of the energy imbalance is substantial from month to month, associated with cloud and weather variations, and interannually mainly associated with ENSO, while the sun affects 15% of the climate change signal on decadal time scales. All estimates (OHC and TOA) show that over the past decade the energy imbalance ranges between about 0.5 and 1 W m−2. By using the full-depth ocean, there is a better overall accounting for energy, but discrepancies remain at interannual time scales between OHC- and TOA-based estimates, notably in 2008/09.

scholarly journals Anthropogenic climate change has changed frequency of past flood during 2010-2013

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yukiko Hirabayashi ◽  
Haireti Alifu ◽  
Dai Yamazaki ◽  
Yukiko Imada ◽  
Hideo Shiogama ◽  
...  
Keyword(s):  
Climate Change ◽  
South America ◽  
Radiative Forcing ◽  
Water Cycle ◽  
Flood Frequency ◽  
Anthropogenic Climate Change ◽  
Climate Change Signal ◽  
Northern Eurasia ◽  
Large Ensemble ◽  
The Impact

AbstractThe ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.

Time-scale dependence of climate-carbon cycle feedbacks for weak perturbations in CMIP5 models

2021 ◽  
Author(s):  
Guilherme Torres Mendonça ◽  
Julia Pongratz ◽  
Christian Reick
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Time Scales ◽  
Radiative Forcing ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Ice Age ◽  
Cmip5 Models ◽  
Global Carbon ◽  
Different Time Scales

<p>The increase in atmospheric CO2 driven by anthropogenic emissions is the main radiative forcing causing climate change. But this increase is not only a result from emissions, but also from changes in the global carbon cycle. These changes arise from feedbacks between climate and the carbon cycle that drive CO2 into or out of the atmosphere in addition to the emissions, thereby either accelerating or buffering climate change. Therefore, understanding the contribution of these feedbacks to the global response of the carbon cycle is crucial in advancing climate research. Currently, this contribution is quantified by the α-β-γ framework (Friedlingstein et al., 2003). But this quantification is only valid for a particular perturbation scenario and time period. In contrast, a recently proposed generalization (Rubino et al., 2016) of this framework for weak perturbations quantifies this contribution for all scenarios and at different time scales. </p><p>Thereby, this generalization provides a systematic framework to investigate the response of the global carbon cycle in terms of the climate-carbon cycle feedbacks. In the present work we employ this framework to study these feedbacks and the airborne fraction in different CMIP5 models. We demonstrate (1) that this generalization of the α-β-γ framework consistently describes the linear dynamics of the carbon cycle in the MPI-ESM; and (2) how by this framework the climate-carbon cycle feedbacks and airborne fraction are quantified at different time scales in CMIP5 models. Our analysis shows that, independently of the perturbation scenario, (1) the net climate-carbon cycle feedback is negative at all time scales; (2) the airborne fraction generally decreases for increasing time scales; and (3) the land biogeochemical feedback dominates the model spread in the airborne fraction at all time scales. This last result therefore emphasizes the need to improve our understanding of this particular feedback.</p><p><strong>References:</strong></p><p>P. Friedlingstein, J.-L. Dufresne, P. Cox, and P. Rayner. How positive is the feedback between climate change and the carbon cycle? Tellus B, 55(2):692–700, 2003.</p><p>M. Rubino, D. Etheridge, C. Trudinger, C. Allison, P. Rayner, I. Enting, R. Mulvaney, L. Steele, R. Langenfelds, W. Sturges, et al. Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake. Nature Geoscience, 9(9):691–694, 2016.</p>

Monitoring the Ocean Heat Content and the Earth Energy imbalance from space altimetry and space gravimetry: the MOHeaCAN product

2021 ◽  
Author(s):  
Marti Florence ◽  
Ablain Michaël ◽  
Fraudeau Robin ◽  
Jugier Rémi ◽  
Meyssignac Benoît ◽  
...  
Keyword(s):  
Time Scales ◽  
Radiant Energy ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Argo Data ◽  
Data Set ◽  
Energy Imbalance ◽  
Temporal Sampling ◽  
The Earth

<p>The Earth Energy Imbalance (EEI) is a key indicator to understand climate change. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m<sup>-2</sup>, compared to the amount of energy entering and leaving the climate system of ~340 W.m<sup>-2</sup>. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m<sup>-2</sup>. It implies that an accuracy of <0.3 W.m<sup>-2</sup> at decadal time scales is necessary to evaluate the long term mean EEI associated with anthropogenic forcing. Ideally an accuracy of <0.1 W.m<sup>-2</sup> at decadal time scales is desirable if we want to monitor future changes in EEI.</p><p>In the frame of the MOHeaCAN project supported by ESA, the EEI indicator is deduced from the global change in Ocean Heat Content (OHC) which is a very good proxy of the EEI since the ocean stores 93% of the excess of heat  gained by the Earth in response to EEI. The OHC is estimated from space altimetry and gravimetry missions (GRACE). This “Altimetry-Gravimetry'' approach is promising because it provides consistent spatial and temporal sampling of the ocean, it samples nearly the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. Consequently, it complements the OHC estimation from the ARGO network. </p><p>The MOHeaCAN product contains monthly time series (between August 2002 and June 2017) of several variables, the main ones being the regional OHC (3°x3° spatial resolution grids), the global OHC and the EEI indicator. Uncertainties are provided for variables at global scale, by propagating errors from sea level measurements (altimetry) and ocean mass content (gravimetry). In order to calculate OHC at regional and global scales, a new estimate of the expansion efficiency of heat at global and regional scales have been performed based on the global ARGO network. </p><p>A scientific validation of the MOHeaCAN product has also been carried out performing thorough comparisons against independent estimates based on ARGO data and on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere. The mean EEI derived from MOHeaCAN product is 0.84 W.m<sup>-2</sup> over the whole period within an uncertainty of ±0.12 W.m<sup>-2</sup> (68% confidence level - 0.20 W.m<sup>-2</sup> at the 90% CL). This figure is in agreement (within error bars at the 90% CL) with other EEI indicators based on ARGO data (e.g. OHC-OMI from CMEMS) although the best estimate is slightly higher. Differences from annual to inter-annual scales have also been observed with ARGO and CERES data. Investigations have been conducted to improve our understanding of the benefits and limitations of each data set to measure EEI at different time scales.</p><p><strong>The MOHeaCAN product from “altimetry-gravimetry” is now available</strong> and can be downloaded at https://doi.org/10.24400/527896/a01-2020.003. Feedback from interested users on this product are welcome.</p>

scholarly journals Insights into Earth’s Energy Imbalance from Multiple Sources

2016 ◽  
Vol 29 (20) ◽  
pp. 7495-7505 ◽  
Author(s):  
Kevin E. Trenberth ◽  
John T. Fasullo ◽  
Karina von Schuckmann ◽  
Lijing Cheng
Keyword(s):  
Heat Content ◽  
System Model ◽  
Multiple Sources ◽  
Short Term ◽  
Ocean Reanalysis ◽  
Energy Imbalance ◽  
Community Earth System Model ◽  
Global Estimates ◽  
Standard Deviations ◽  
Radiation Measurements

Abstract The current Earth’s energy imbalance (EEI) can best be estimated from changes in ocean heat content (OHC), complemented by top-of-atmosphere (TOA) radiation measurements and an assessment of the small non-ocean components. Sustained observations from the Argo array of autonomous profiling floats enable near-global estimates of OHC since 2005, which reveal considerable cancellation of variations in the upper 300 m. An analysis of the monthly contributions to EEI from non-ocean components (land and ice) using the Community Earth System Model (CESM) Large Ensemble reveals standard deviations of 0.3–0.4 W m−2 (global); largest values occur in August, but values are below 0.75 W m−2 greater than 95% of the time. Global standard deviations of EEI of 0.64 W m−2 based on top-of-atmosphere observations therefore substantially constrain ocean contributions, given by the tendencies of OHC. Instead, monthly standard deviations of many Argo-based OHC tendencies are 6–13 W m−2, and nonphysical fluctuations are clearly evident. It is shown that an ocean reanalysis with multivariate dynamical data assimilation features much better agreement with TOA radiation, and 44% of the vertically integrated short-term OHC trend for 2005–14 of 0.8 ± 0.2 W m−2 (globally) occurs below 700-m depth. Largest warming occurs from 20° to 50°S, especially over the southern oceans, and near 40°N in all ocean analyses. The EEI is estimated to be 0.9 ± 0.3 W m−2 for 2005–14.

scholarly journals Sensitivity of atmospheric CO<sub>2</sub> and climate to explosive volcanic eruptions

2011 ◽  
Vol 8 (2) ◽  
pp. 2957-3007 ◽  
Author(s):  
T. L. Frölicher ◽  
F. Joos ◽  
C. C. Raible
Keyword(s):  
Global Warming ◽  
Carbon Cycle ◽  
Surface Temperature ◽  
Time Scales ◽  
Radiative Forcing ◽  
Volcanic Eruptions ◽  
Atmospheric Co2 ◽  
Dynamical Response ◽  
Steric Sea Level ◽  
Explosive Volcanic Eruptions

Abstract. Impacts of low-latitude, explosive volcanic eruptions on climate and the carbon cycle are quantified by forcing a comprehensive, fully coupled carbon cycle-climate model with pulse-like stratospheric sulfur release. The model represents the radiative and dynamical response of the climate system to volcanic eruptions and simulates a decrease of global and regional atmospheric surface temperature, regionally distinct changes in precipitation, a positive phase of the North Atlantic Oscillation, and a decrease in atmospheric CO2 after volcanic eruptions. The volcanic-induced cooling reduces overturning rates in tropical soils, which dominates over reduced litter input due to soil moisture decrease, resulting in higher land carbon inventories for several decades. The perturbation in the ocean carbon inventory changes sign from an initially weak carbon sink to a carbon source. Positive carbon and negative temperature anomalies in subsurface waters last up to several decades. The multi-decadal decrease in atmospheric CO2 yields an additional radiative forcing that amplifies the cooling and perturbs the Earth System on much longer time scales than the atmospheric residence time of volcanic aerosols. In addition, century-scale global warming simulations with and without volcanic eruptions over the historical period show that the ocean integrates volcanic radiative cooling and responds for different physical and biogeochemical parameters such as steric sea level or dissolved oxygen. Results from a suite of sensitivity simulations with different amounts of sulfur released and from global warming simulations show that the carbon cycle-climate sensitivity γ, expressed as change in atmospheric CO2 per unit change in global mean surface temperature, depends on the perturbation. On decadal time scales, modeled γ is several times larger for a Pinatubo-like eruption than for the industrial period and for a high emission, 21st century scenario.

scholarly journals Multidecadal ENSO Amplitude Variability in a 1000-yr Simulation of a Coupled Global Climate Model: Implications for Observed ENSO Variability

2013 ◽  
Vol 26 (23) ◽  
pp. 9399-9407 ◽  
Author(s):  
Simon Borlace ◽  
Wenju Cai ◽  
Agus Santoso
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Climate Model ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Warming Trend ◽  
Recent Change ◽  
Climate Change Signal ◽  
Coupled Global Climate Model

The amplitude of the El Niño–Southern Oscillation (ENSO) can vary naturally over multidecadal time scales and can be influenced by climate change. However, determining the mechanism for this variation is difficult because of the paucity of observations over such long time scales. Using a 1000-yr integration of a coupled global climate model and a linear stability analysis, it is demonstrated that multidecadal modulation of ENSO amplitude can be driven by variations in the governing dynamics. In this model, the modulation is controlled by the underlying thermocline feedback mechanism, which in turn is governed by the response of the oceanic thermocline slope across the equatorial Pacific to changes in the overlying basinwide zonal winds. Furthermore, the episodic strengthening and weakening of this coupled interaction is shown to be linked to the slowly varying background climate. In comparison with the model statistics, the recent change of ENSO amplitude in observations appears to be still within the range of natural variability. This is despite the apparent warming trend in the mean climate. Hence, this study suggests that it may be difficult to infer a climate change signal from changes in ENSO amplitude alone, particularly given the presently limited observational data.

scholarly journals Impact of climate change on volcanic processes: current understanding and future challenges

2021 ◽  
Author(s):  
Thomas Aubry ◽  
Jamie Farquharson ◽  
Colin Rowell ◽  
Sebastian Watt ◽  
Virginie Pinel ◽  
...  
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Climate Changes ◽  
Numerical Models ◽  
Anthropogenic Activities ◽  
Volcanic Eruptions ◽  
Climate Impacts ◽  
Volcanic Plume ◽  
Atmospheric Conditions ◽  
Climate Conditions

The impacts of volcanic eruptions on climate are increasingly well understood, but the mirror question of how climate changes affect volcanic systems and processes, which we term “climate-volcano impacts”, remains understudied. Accelerating research on this topic is critical in view of rapid climate change driven by anthropogenic activities. Over the last two decades, we have improved our understanding of how mass distribution on the Earth’s surface, in particular changes in ice and water distribution linked to glacial cycles, affects mantle melting, crustal magmatic processing and eruption rates. New hypotheses on the impacts of climate change on eruption processes have also emerged, including how eruption style and volcanic plume rise are affected by changing surface and atmospheric conditions, and how volcanic sulfate aerosol lifecycle, radiative forcing and climate impacts are modulated by background climate conditions. Future improvements in past climate reconstructions and current climate observations, volcanic eruption records and volcano monitoring, and numerical models will contribute to boost research on climate-volcano impacts. Important mechanisms remain to be explored, such as how changes in atmospheric circulation and precipitation will affect the volcanic ash lifecycle. Fostering a holistic and interdisciplinary approach to climate-volcano impacts is critical to gain a full picture of how ongoing climate changes may affect the environmental and societal impacts of volcanic activity.

scholarly journals Spatio-Temporal Variability in Remotely Sensed Vegetation Greenness Across Yellowstone National Park

2019 ◽  
Vol 11 (7) ◽  
pp. 798 ◽  
Author(s):  
Michael Notaro ◽  
Kristen Emmett ◽  
Donal O’Leary
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Yellowstone National Park ◽  
National Park ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Climate Change Signal ◽  
Summer Drought ◽  
Remotely Sensed Data ◽  
Vegetation Greenness

The study’s objective was to quantify the responses of vegetation greenness and productivity to climate variability and change across complex topographic, climatic, and ecological gradients in Yellowstone National Park through the use of remotely sensed data. The climate change signal in Yellowstone was pronounced, including substantial warming, an abrupt decline in snowpack, and more frequent droughts. While phenological studies are increasing in Yellowstone, the near absence of long-term and continuous ground-based phenological measurements motivated the study’s application of remotely sensed data to aid in identifying ecological vulnerabilities and guide resource management in light of on ongoing environmental change. Correlation, time-series, and empirical orthogonal function analyses for 1982–2015 focused on Daymet data and vegetation indices (VIs) from the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The study’s key questions address unique time scales. First, what are the dominant meteorological drivers of variability in vegetation greenness on seasonal to interannual time scales? Key results include: (1) Green-up is the most elevation- and climate-sensitive phenological stage, with La Niña-induced cool, wet conditions or an anomalously deep snowpack delaying the green-up wave. (2) Drought measures were the dominant contributors towards phenological variability, as winter–spring drought corresponded to enhanced April–June greening and spring–summer drought corresponded to reduced August–September greening. Second, how have patterns of productivity changed in response to climate change and disturbances? Key results include: (1) The park predominantly exhibited positive productivity trends, associated with lodgepole pine re-establishment and growth following the 1988 fires. (2) Landscapes which were undisturbed by the 1988 fires showed no apparent sign of warming-induced greening. This study motivates a systematic investigation of remote-sensing data across western parks to identify ecological vulnerabilities and support the development of climate change vulnerability assessments and adaptation strategies.

A new project to monitor the Ocean Heat Content and the Earth Energy imbalance from space: MOHeaCAN

2020 ◽  
Author(s):  
Michaël Ablain ◽  
Benoit Meyssignac ◽  
Alejandro Blazquez ◽  
Marti Florence ◽  
Rémi Jugier ◽  
...  
Keyword(s):  
Time Scales ◽  
Radiant Energy ◽  
Heat Content ◽  
Deep Ocean ◽  
Heat Fluxes ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Energy Imbalance ◽  
The Earth

&lt;p&gt;The Earth Energy Imbalance (EEI) is a key indicator to understand the Earth&amp;#8217;s changing. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m-2, compared to the amount of energy entering and leaving the climate system of ~340 W.m-2. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. It implies that an accuracy of &lt;0.3 W.m-2 at decadal time scales is necessary to evaluate the long term mean EEI associated with anthropogenic forcing. Ideally an accuracy of &lt;0.1 W.m-2 at decadal time scales is desirable if we want to monitor future changes in EEI. The ocean heat content (OHC) is a very good proxy to estimate EEI as ocean concentrates the vast majority of the excess of energy (~93%) associated with EEI. Several methods exist to estimate OHC:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;the direct measurement of in situ temperature based on temperature/Salinity profiles (e.g. ARGO floats),&lt;/li&gt; &lt;li&gt;the measurement of the net ocean surface heat fluxes from space (CERES),&lt;/li&gt; &lt;li&gt;the estimate from ocean reanalyses that assimilate observations from both satellite and in situ instruments,&lt;/li&gt; &lt;li&gt;the measurement of the thermal expansion of the ocean from space based on differences between the total sea-level content derived from altimetry measurements and the mass content derived from GRACE data (noted &amp;#8220;Altimetry-GRACE&amp;#8221;).&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;To date, the best results are given by the first method based on ARGO network. However ARGO measurements do no sample deep ocean below 2000 m depth and marginal seas as well as the ocean below sea ice. Re-analysis provides a more complete estimation but large biases in the polar oceans and spurious drifts in the deep ocean mask a significant part of the OHC signal related to EEI. The method based on estimation of ocean net heat fluxes (CERES) is not appropriate for OHC calculation due to a too strong uncertainty (&amp;#177;15 W.m-2).&amp;#160;&lt;/p&gt;&lt;p&gt;In the MOHeaCAN project supported by ESA, we are being developed the &amp;#8220;Altimetry-GRACE&amp;#8221; approach&amp;#160; which is promising since it provides consistent spatial and temporal sampling of the ocean, it samples the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean&amp;#8217;s entire depth. Consequently, it complements the OHC estimation from ARGO.&amp;#160; However, to date the uncertainty in OHC from this method is close to 0.5 W.m-2, and thus greater than the requirement of 0.3 W.m-2 needed to a good EEI estimation. Therefore the scientific objective of the MOHeaCan project is&amp;#160; to improve these estimates :&lt;/p&gt;&lt;ol&gt;&lt;li&gt;by developing novel algorithms in order to reach the challenging target for the uncertainty quantification of 0.3 W. m&amp;#8722;2;&lt;/li&gt; &lt;li&gt;by estimating realistic OHC uncertainties thanks to an error budget of measurements applying a rigorous mathematical formalism;&lt;/li&gt; &lt;li&gt;by developing a software prototype systems that allow to perform sensitivities studies and OHC product and its uncertainty generation;&lt;/li&gt; &lt;li&gt;by assessing our estimation by performing comparison against independent estimates based on ARGO network, and based on the Clouds and the Earth&amp;#8217;s Radiant energy System (CERES) measurements at the top of the atmosphere.&lt;/li&gt; &lt;/ol&gt;

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